Method, apparatus and computer program product for processing of images

ABSTRACT

In accordance with an example embodiment a method, apparatus and computer program product are provided. The method comprises facilitating receipt of a source image and compressing a plurality of images based on the source image. The plurality of images is associated with compression values generated on compression of the plurality of images. The method further comprises identifying an at least partially matching image to the source image from among the plurality of images based on the compression values.

TECHNICAL FIELD

Various implementations relate generally to method, apparatus, andcomputer program product for processing of images.

BACKGROUND

The rapid advancement in technology related to capturing images hasresulted in an exponential increase in the creation of image content.Devices like mobile phones and personal digital assistants (PDA) are nowbeing increasingly configured with image capturing tools, such as acamera, thereby facilitating easy capture of the image content. Thecaptured images may be subjected to processing based on various userneeds. For example, images corresponding to a scene captured fromvarious viewpoints and angles may have a high amount of overlappingimage portions. Such images may be processed to generate a panoramaimage. A panorama image refers to an image with an extended field ofview (for example, a wide-angle representation) beyond that can becaptured by an image sensor. The processing of images may also help inretrieving relatively similar images or deleting duplicate images from alarge collection of images and help streamline browsing and storing ofthe images.

SUMMARY OF SOME EMBODIMENTS

Various aspects of example embodiments are set out in the claims.

In a first aspect, there is provided a method comprising: facilitatingreceipt of a source image; compressing a plurality of images based onthe source image, wherein the plurality of images are associated withcompression values generated on compression of the plurality of images;and identifying an at least partially matching image to the source imagefrom among the plurality of images based on the compression values.

In a second aspect, there is provided an apparatus comprising at leastone processor; and at least one memory comprising computer program code,the at least one memory and the computer program code configured to,with the at least one processor, cause the apparatus to at leastperform: facilitate receipt of a source image; compress a plurality ofimages based on the source image, wherein the plurality of images areassociated with compression values generated on compression of theplurality of images; and identify an at least partially matching imageto the source image from among the plurality of images based on thecompression values.

In a third aspect, there is provided a computer program productcomprising at least one computer-readable storage medium, thecomputer-readable storage medium comprising a set of instructions,which, when executed by one or more processors, cause an apparatus to atleast perform: facilitate receipt of a source image; compress aplurality of images based on the source image, wherein the plurality ofimages are associated with compression values generated on compressionof the plurality of images; and identify an at least partially matchingimage to the source image from among the plurality of images based onthe compression values.

In a fourth aspect, there is provided an apparatus comprising: means forfacilitating receipt of a source image; means for compressing aplurality of images based on the source image, wherein the plurality ofimages are associated with compression values generated on compressionof the plurality of images; and means for identifying an at leastpartially matching image to the source image from among the plurality ofimages based on the compression values.

In a fifth aspect, there is provided a computer program comprisingprogram instructions which when executed by an apparatus, cause theapparatus to: facilitate receipt of a source image; compress a pluralityof images based on the source image, wherein the plurality of images areassociated with compression values generated on compression of theplurality of images; and identify an at least partially matching imageto the source image from among the plurality of images based on thecompression values.

BRIEF DESCRIPTION OF THE FIGURES

Various embodiments are illustrated by way of example, and not by way oflimitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a device in accordance with an example embodiment;

FIG. 2 illustrates an apparatus for processing of images in accordancewith an example embodiment;

FIG. 3 illustrates a simplified logical overview for identifying an atleast partially matching image in accordance with an example embodiment;

FIG. 4 illustrates determination of a spatial order of images forpanorama image generation in accordance with an example embodiment;

FIG. 5 is a flowchart depicting an example method for processing ofimages in accordance with an example embodiment; and

FIGS. 6A and 6B illustrate a flowchart depicting an example method forprocessing of images in accordance with another example embodiment.

DETAILED DESCRIPTION

Example embodiments and their potential effects are understood byreferring to FIGS. 1 through 6B of the drawings.

FIG. 1 illustrates a device 100 in accordance with an exampleembodiment. It should be understood, however, that the device 100 asillustrated and hereinafter described is merely illustrative of one typeof device that may benefit from various embodiments, therefore, shouldnot be taken to limit the scope of the embodiments. As such, it shouldbe appreciated that at least some of the components described below inconnection with the device 100 may be optional and thus in an exampleembodiment may include more, less or different components than thosedescribed in connection with the example embodiment of FIG. 1. Thedevice 100 could be any of a number of types of mobile electronicdevices, for example, portable digital assistants (PDAs), pagers, mobiletelevisions, gaming devices, cellular phones, all types of computers(for example, laptops, mobile computers or desktops), cameras,audio/video players, radios, global positioning system (GPS) devices,media players, mobile digital assistants, or any combination of theaforementioned, and other types of communications devices.

The device 100 may include an antenna 102 (or multiple antennas) inoperable communication with a transmitter 104 and a receiver 106. Thedevice 100 may further include an apparatus, such as a controller 108 orother processing device that provides signals to and receives signalsfrom the transmitter 104 and receiver 106, respectively. The signals mayinclude signaling information in accordance with the air interfacestandard of the applicable cellular system, and/or may also include datacorresponding to user speech, received data and/or user generated data.In this regard, the device 100 may be capable of operating with one ormore air interface standards, communication protocols, modulation types,and access types. By way of illustration, the device 100 may be capableof operating in accordance with any of a number of first, second, thirdand/or fourth-generation communication protocols or the like. Forexample, the device 100 may be capable of operating in accordance withsecond-generation (2G) wireless communication protocols IS-136 (timedivision multiple access (TDMA)), GSM (global system for mobilecommunication), and IS-95 (code division multiple access (CDMA)), orwith third-generation (3G) wireless communication protocols, such asUniversal Mobile Telecommunications System (UMTS), CDMA1000, widebandCDMA (WCDMA) and time division-synchronous CDMA (TD-SCDMA), with 3.9Gwireless communication protocol such as evolved-universal terrestrialradio access network (E-UTRAN), with fourth-generation (4G) wirelesscommunication protocols, or the like. As an alternative (oradditionally), the device 100 may be capable of operating in accordancewith non-cellular communication mechanisms. For example, computernetworks such as the Internet, local area network, wide area networks,and the like; short range wireless communication networks such asBluetooth® networks, Zigbee® networks, Institute of Electric andElectronic Engineers (IEEE) 802.11x networks, and the like; wirelinetelecommunication networks such as public switched telephone network(PSTN).

The controller 108 may include circuitry implementing, among others,audio and logic functions of the device 100. For example, the controller108 may include, but are not limited to, one or more digital signalprocessor devices, one or more microprocessor devices, one or moreprocessor(s) with accompanying digital signal processor(s), one or moreprocessor(s) without accompanying digital signal processor(s), one ormore special-purpose computer chips, one or more field-programmable gatearrays (FPGAs), one or more controllers, one or moreapplication-specific integrated circuits (ASICs), one or morecomputer(s), various analog to digital converters, digital to analogconverters, and/or other support circuits. Control and signal processingfunctions of the device 100 are allocated between these devicesaccording to their respective capabilities. The controller 108 thus mayalso include the functionality to convolutionally encode and interleavemessage and data prior to modulation and transmission. The controller108 may additionally include an internal voice coder, and may include aninternal data modem. Further, the controller 108 may includefunctionality to operate one or more software programs, which may bestored in a memory. For example, the controller 108 may be capable ofoperating a connectivity program, such as a conventional Web browser.The connectivity program may then allow the device 100 to transmit andreceive Web content, such as location-based content and/or other webpage content, according to a Wireless Application Protocol (WAP),Hypertext Transfer Protocol (HTTP) and/or the like. In an exampleembodiment, the controller 108 may be embodied as a multi-core processorsuch as a dual or quad core processor. However, any number of processorsmay be included in the controller 108.

The device 100 may also comprise a user interface including an outputdevice such as a ringer 110, an earphone or speaker 112, a microphone114, a display 116, and a user input interface, which may be coupled tothe controller 108. The user input interface, which allows the device100 to receive data, may include any of a number of devices allowing thedevice 100 to receive data, such as a keypad 118, a touch display, amicrophone or other input device. In embodiments including the keypad118, the keypad 118 may include numeric (0-9) and related keys (#, *),and other hard and soft keys used for operating the device 100.Alternatively or additionally, the keypad 118 may include a conventionalQWERTY keypad arrangement. The keypad 118 may also include various softkeys with associated functions. In addition, or alternatively, thedevice 100 may include an interface device such as a joystick or otheruser input interface. The device 100 further includes a battery 120,such as a vibrating battery pack, for powering various circuits that areused to operate the device 100, as well as optionally providingmechanical vibration as a detectable output.

In an example embodiment, the device 100 includes a media capturingelement, such as a camera, video and/or audio module, in communicationwith the controller 108. The media capturing element may be any meansfor capturing an image, video and/or audio for storage, display ortransmission. In an example embodiment, the media capturing element is acamera module 122 which may include a digital camera capable of forminga digital image file from a captured image. As such, the camera module122 includes all hardware, such as a lens or other optical component(s),and software for creating a digital image file from a captured image.Alternatively, or additionally, the camera module 122 may include thehardware needed to view an image, while a memory device of the device100 stores instructions for execution by the controller 108 in the formof software to create a digital image file from a captured image. In anexample embodiment, the camera module 122 may further include aprocessing element such as a co-processor, which assists the controller108 in processing image data and an encoder and/or decoder forcompressing and/or decompressing image data. The encoder and/or decodermay encode and/or decode according to a JPEG standard format or anotherlike format. For video, the encoder and/or decoder may employ any of aplurality of standard formats such as, for example, standards associatedwith H.261, H.262/MPEG-2, H.263, H.264, H.264/MPEG-4, MPEG-4, and thelike. In some cases, the camera module 122 may provide live image datato the display 116. In an example embodiment, the display 116 may belocated on one side of the device 100 and the camera module 122 mayinclude a lens positioned on the opposite side of the device 100 withrespect to the display 116 to enable the camera module 122 to captureimages on one side of the device 100 and present a view of such imagesto the user positioned on the other side of the device 100.

The device 100 may further include a user identity module (UIM) 124. TheUIM 124 may be a memory device having a processor built in. The UIM 124may include, for example, a subscriber identity module (SIM), auniversal integrated circuit card (UICC), a universal subscriberidentity module (USIM), a removable user identity module (R-UIM), or anyother smart card. The UIM 124 typically stores information elementsrelated to a mobile subscriber. In addition to the UIM 124, the device100 may be equipped with memory. For example, the device 100 may includevolatile memory 126, such as volatile random access memory (RAM)including a cache area for the temporary storage of data. The device 100may also include other non-volatile memory 128, which may be embeddedand/or may be removable. The non-volatile memory 128 may additionally oralternatively comprise an electrically erasable programmable read onlymemory (EEPROM), flash memory, hard drive, or the like. The memories maystore any number of pieces of information, and data, used by the device100 to implement the functions of the device 100.

FIG. 2 illustrates an apparatus 200 for processing of images inaccordance with an example embodiment. The apparatus 200 for processingof images may be employed, for example, in the device 100 of FIG. 1.However, it should be noted that the apparatus 200, may also be employedon a variety of other devices both mobile and fixed, and therefore,embodiments should not be limited to application on devices such as thedevice 100 of FIG. 1. Alternatively, embodiments may be employed on acombination of devices including, for example, those listed above.Accordingly, various embodiments may be embodied wholly at a singledevice, (for example, the device 100 or in a combination of devices). Itshould also be noted that the devices or elements described below maynot be mandatory and thus some may be omitted in certain embodiments.

In an embodiment, the images may be captured by utilizing the cameramodule 122 of the device 100, and stored in the memory of the device100. In an embodiment, the images may correspond to a same scene, oralternatively, the images may correspond to disparate scenes. The imagesmay be stored in the internal memory such as hard drive, random accessmemory (RAM) of the apparatus 100 or in external storage medium such asdigital versatile disk, compact disk, flash drive, memory card, or fromexternal storage locations through Internet, Bluetooth®, and the like.

The apparatus 200 includes or otherwise is in communication with atleast one processor 202 and at least one memory 204. Examples of the atleast one memory 204 include, but are not limited to, volatile and/ornon-volatile memories. Some examples of the volatile memory include, butare not limited to, random access memory, dynamic random access memory,static random access memory, and the like. Some example of thenon-volatile memory includes, but are not limited to, hard disks,magnetic tapes, optical disks, programmable read only memory, erasableprogrammable read only memory, electrically erasable programmable readonly memory, flash memory, and the like. The memory 204 may beconfigured to store information, data, applications, instructions or thelike for enabling the apparatus 200 to carry out various functions inaccordance with various example embodiments. For example, the memory 204may be configured to buffer input data comprising multimedia content forprocessing by the processor 202. Additionally or alternatively, thememory 204 may be configured to store instructions for execution by theprocessor 202.

An example of the processor 202 may include the controller 108. Theprocessor 202 may be embodied in a number of different ways. Theprocessor 202 may be embodied as a multi-core processor, a single coreprocessor; or combination of multi-core processors and single coreprocessors. For example, the processor 202 may be embodied as one ormore of various processing means such as a coprocessor, amicroprocessor, a controller, a digital signal processor (DSP),processing circuitry with or without an accompanying DSP, or variousother processing devices including integrated circuits such as, forexample, an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a microcontroller unit (MCU), a hardwareaccelerator, a special-purpose computer chip, or the like. In an exampleembodiment, the multi-core processor may be configured to executeinstructions stored in the memory 204 or otherwise accessible to theprocessor 202. Alternatively or additionally, the processor 202 may beconfigured to execute hard coded functionality. As such, whetherconfigured by hardware or software methods, or by a combination thereof,the processor 202 may represent an entity, for example, physicallyembodied in circuitry, capable of performing operations according tovarious embodiments while configured accordingly. For example, if theprocessor 202 is embodied as two or more of an ASIC, FPGA or the like,the processor 202 may be specifically configured hardware for conductingthe operations described herein. Alternatively, as another example, ifthe processor 202 is embodied as an executor of software instructions,the instructions may specifically configure the processor 202 to performthe algorithms and/or operations described herein when the instructionsare executed. However, in some cases, the processor 202 may be aprocessor of a specific device, for example, a mobile terminal ornetwork device adapted for employing embodiments by furtherconfiguration of the processor 202 by instructions for performing thealgorithms and/or operations described herein. The processor 202 mayinclude, among other things, a clock, an arithmetic logic unit (ALU) andlogic gates configured to support operation of the processor 202.

A user interface 206 may be in communication with the processor 202.Examples of the user interface 206 include, but are not limited to,input interface and/or output user interface. The input interface isconfigured to receive an indication of a user input. The output userinterface provides an audible, visual, mechanical or other output and/orfeedback to the user. Examples of the input interface may include, butare not limited to, a keyboard, a mouse, a joystick, a keypad, a touchscreen, soft keys, and the like. Examples of the output interface mayinclude, but are not limited to, a display such as light emitting diodedisplay, thin-film transistor (TFT) display, liquid crystal displays,active-matrix organic light-emitting diode (AMOLED) display, amicrophone, a speaker, ringers, vibrators, and the like. In an exampleembodiment, the user interface 206 may include, among other devices orelements, any or all of a speaker, a microphone, a display, and akeyboard, touch screen, or the like. In this regard, for example, theprocessor 202 may comprise user interface circuitry configured tocontrol at least some functions of one or more elements of the userinterface 206, such as, for example, a speaker, ringer, microphone,display, and/or the like. The processor 202 and/or user interfacecircuitry comprising the processor 202 may be configured to control oneor more functions of one or more elements of the user interface 206through computer program instructions, for example, software and/orfirmware, stored on a memory, for example, the at least one memory 204,and/or the like, accessible to the processor 202.

In an example embodiment, the apparatus 200 may include an electronicdevice. Some examples of the electronic device include communicationdevice, media capturing device with communication capabilities,computing devices, and the like. Some examples of the communicationdevice may include a mobile phone, a personal digital assistant (PDA),and the like. Some examples of computing device may include a laptop, apersonal computer, and the like. In an example embodiment, theelectronic device may include a user interface, for example, the UI 206,having user interface circuitry and user interface software configuredto facilitate a user to control at least one function of the electronicdevice through use of a display and further configured to respond touser inputs. In an example embodiment, the electronic device may includea display circuitry configured to display at least a portion of the userinterface of the electronic device. The display and display circuitrymay be configured to facilitate the user to control at least onefunction of the electronic device.

In an example embodiment, the electronic device may be embodied as toinclude a transceiver. The transceiver may be any device operating orcircuitry operating in accordance with software or otherwise embodied inhardware or a combination of hardware and software. For example, theprocessor 202 operating under software control, or the processor 202embodied as an ASIC or FPGA specifically configured to perform theoperations described herein, or a combination thereof, therebyconfigures the apparatus or circuitry to perform the functions of thetransceiver. The transceiver may be configured to receive images. In anembodiment, the images correspond to a scene.

In an example embodiment, the electronic device may be embodied as toinclude an image sensor, such as an image sensor 208. The image sensor208 may be in communication with the processor 202 and/or othercomponents of the apparatus 200. The image sensor 208 may be incommunication with other imaging circuitries and/or software, and isconfigured to capture digital images or to make a video or other graphicmedia files. The image sensor 208 and other circuitries, in combination,may be an example of the camera module 122 of the device 100. In certainexample embodiments, the image sensor 208 may be external to theapparatus 200, but accessible and/or controlled by the apparatus 200.

These components (202-208) may communicate with each other via acentralized circuit system 210 for capturing of image and/or videocontent. The centralized circuit system 210 may be various devicesconfigured to, among other things, provide or enable communicationbetween the components (202-208) of the apparatus 200. In certainembodiments, the centralized circuit system 210 may be a central printedcircuit board (PCB) such as a motherboard, main board, system board, orlogic board. The centralized circuit system 210 may also, oralternatively, include other printed circuit assemblies (PCAs) orcommunication channel media.

In an example embodiment, the processor 202 is configured to, with thecontent of the memory 204, and optionally with other componentsdescribed herein, to cause the apparatus 200 to facilitate receipt of asource image. In an embodiment, an image for use in processing a set ofimages (for example, images with or without overlapping image portionsand/or regions) may be referred to as a source image. In an embodiment,the source image may be an image captured corresponding to a scene by acamera module 122 of device 100. The scene may include one or moreobjects in a surrounding environment of the apparatus 200, for example,a person or a gathering of individuals, birds, books, a playground,natural scenery, such as a mountain, and the like. In an embodiment, thesource image may be received from the image sensor 208. It is noted thatthe image sensor 208 (or the camera module 122) may capture framescorresponding to the scene and the term ‘frames’ and ‘images’ have beenused interchangeably herein. In an embodiment, multiple imagescorresponding to a scene may be captured by the image sensor 208. Aninitial image (for example, the image with the earliest timestamp) fromamong the captured images may be dynamically chosen as the source image.In an embodiment, a source image may be selected from among a collectionof images either automatically or manually by user selection. In anembodiment, a user may provide a selection of the source image fromamong a collection of images, for example by using the user interface206. In an embodiment, the source image may be received from an internalmemory such as hard drive, random access memory (RAM) of the apparatus200 or from an external storage medium such as digital versatile disk,compact disk, flash drive, memory card, or from external storagelocations through Internet, Bluetooth®, and the like. The source imagemay also be received from the memory 204. In an example embodiment, aprocessing means may be configured to facilitate receipt of the sourceimage. An example of the processing means may include the processor 202,which may be an example of the controller 108.

In an example embodiment, the processor 202 is configured to, with thecontent of the memory 204, and optionally with other componentsdescribed herein, to cause the apparatus 200 to compress a plurality ofimages based on the source image. In an embodiment, the image sensor 208may capture images corresponding to a scene from various angles andviewpoints. The various angles and viewpoints for capturing the imagescorresponding to the scene may be achieved by spanning the image sensor208 in one or more directions. In an example embodiment, the imagesensor 208 may be spanned through 360 degrees during image capture toobtain multiple images corresponding to the scene. The captured imagesmay include overlapping image portions and/or regions on account of theimages corresponding to the same scene being captured from differentangles and viewpoints. The initial captured image from among themultiple images may be chosen as the source image whereas the remainingimages of the multiple captured images may configure the plurality ofimages to be compressed based on the source image. It is noted that thesource image and the plurality of images together configure the imagescaptured by the image sensor 208 corresponding to the scene and compriseoverlapping image portions and/or regions. In an embodiment, the sourceimage may be chosen from a collection of images stored in an internalmemory of the apparatus 200 or in the memory 204, and one or more imagesfrom the remaining images from the collection may configure theplurality of images to be compressed based on the source image. In anembodiment, the source image may be received from a user and theplurality of images may be received via network. Examples of the networkmay include a wired network, a wireless network and/or a combinationthereof. Examples of the wired network include but are not limited tolocal area network, wide area network, Ethernet and the like. Examplesof the wireless network include but are not limited to cellular network,Wi-Fi network, wireless LAN, Zigbee network and the like. An example ofcombination of the wired network and the wireless network may includebut is not limited to the Internet. In an example embodiment, aprocessing means may be configured to compress the plurality of imagesbased on the source image. An example of the processing means mayinclude the processor 202, which may be an example of the controller108.

In an example embodiment, compressing the plurality of images based onthe source image comprises extracting features from the source image andquantizing the extracted features to create a plurality of codescorresponding to the source image. In an embodiment, the extractedfeatures are capable of characterizing the source image. Examples of thefeature points may include, but are not limited to, corners, edges of animage, or other region of interest such as background of the scene. Inan example embodiment, the apparatus 200 may be caused to use algorithmssuch as scale-invariant feature transform (SIFT), Harris cornerdetector, smallest univalue segment assimilating nucleus (SUSAN) cornerdetector, features from accelerated segment test (FAST) for determiningfeature points associated with the source image. In an embodiment,extracting the features from the source image comprises applying adiscrete cosine transform (DCT), a discrete sine transform (DST), aKarhunen-Loève theorem (KLT) transform or a Hadamard transform onmacroblocks corresponding to the source image. In an example embodiment,the DCT is applied on macroblocks corresponding to the source image andthe DC components thus obtained may be treated as features correspondingto the source image. In an embodiment, the DC components may be obtainedby partially decoding the source image. On extracting the features ofthe source image, the features may be subjected to quantization, whereeach feature is scaled corresponding to a scale-down factor (alsoreferred to as the quantization parameter) to generate an optimal set ofplurality of codes. In an example embodiment, a processing means may beconfigured to extract features from the source image and quantize theextracted features to create the plurality of codes corresponding to thesource image. An example of the processing means may include theprocessor 202, which may be an example of the controller 108.

In an embodiment, compressing the plurality of images based on thesource image further comprises generating a compression table bycompressing the source image based on the plurality of codes. In anembodiment, the source image is compressed using a lossless compressionscheme for generating the compression table from the plurality of codes.In an embodiment, the lossless compression scheme is an arithmeticcoding scheme, Huffman coding scheme, context coding scheme, Lempel-Zivcompression scheme or Lempel-Ziv-Welch (LZW) compression scheme. Forexample, the source image and the plurality of codes generated byquantizing the features corresponding to the source image may beprovided as an input to LZW compression scheme, which may provide anoutput as the compression table. In an embodiment, the compression tablemay comprise a pattern of matrix of coefficients characterizing thesource image. In an embodiment, the compression table is constructed byscanning in a plurality of directions. For example, in an embodiment thecompression table may be constructed by scanning in non-orthogonaldirections in addition to a horizontal direction and a verticaldirection. In an example embodiment, a processing means may beconfigured to generate a compression table by compressing the sourceimage based on the plurality of codes. An example of the processingmeans may include the processor 202, which may be an example of thecontroller 108.

In an embodiment, the plurality of images is compressed based on thegenerated compression table corresponding to the source image. Asexplained above, the compression table is generated based on compressingthe source image based on the plurality of codes. The compression tableand an image of the plurality of images may be provided as an input to alossless compression scheme, such as the LZW compression scheme, tocompress the image. The plurality of images may be compressed in asimilar manner based on the generated compression table. In anembodiment, an image may be associated with a compression valuegenerated on compression of the image based on the compression table. Inan embodiment, the compression value may be configured to provide anindication of the extent of compression achieved based on thecompression table corresponding to the source image. In an embodiment, ahigher compression value corresponds to a higher similarity (forexample, higher amount of overlapping image portions and/or regions)between the source image and the image subjected to compression.Similarly, a lower compression value corresponds to a fewer similarities(for example, lesser amount of overlapping image portions and/orregions) between the source image and the image subjected tocompression. On performing compression of the plurality of images basedon the source image, a plurality of compression values may be generated.In an embodiment, each image of the plurality of images may beassociated with a compression value generated on compression of the eachimage. In an embodiment, one or more images of the plurality of imagesmay be associated with compression values generated on compression ofthe one or more images. In an example embodiment, a processing means maybe configured to compress the plurality of images based on the generatedcompression table corresponding to the source image. An example of theprocessing means may include the processor 202, which may be an exampleof the controller 108.

In an example embodiment, the processor 202 is configured to, with thecontent of the memory 204, and optionally with other componentsdescribed herein, to cause the apparatus 200 to identify an at leastpartially matching image to the source image from among the plurality ofimages based on the compression values. As explained, the plurality ofimages may be compressed based on the source image and the compressedimages may be associated with compression values. As the compressionvalues provide an indication of the extent of compression achieved, theimage with a highest compression value may be treated as the image to beat least partially matching image. For example, relatively similar, tothe source image. In an embodiment, the at least partially matchingimage is identified based on the highest compression value from amongthe compression values. It is noted that the at least partially matchingimage may include overlapping image portions and/or regions with thesource image to a degree higher than the remaining images from among theplurality of images. The identification of the at least partiallymatching image is further explained with an illustrative example asfollows: multiple images may be captured corresponding to a scene fromvarious viewpoints and angles. Such images may include overlapping imageportions and/or regions. The first image, for example the image with theearliest timestamp from among the multiple images may be chosen as thesource image while the remaining images may configure the plurality ofimages. The plurality of images may be compressed based on the sourceimage, for example using the compression table, and compression valuescorresponding to the plurality of images generated. The image from amongthe plurality of images associated with the highest compression valuemay be identified as the at least partially matching image to the sourceimage. In an example embodiment, a processing means may be configured toidentify an at least partially matching image to the source image fromamong the plurality of images based on compression values. An example ofthe processing means may include the processor 202, which may be anexample of the controller 108.

In an example embodiment, the processor 202 is configured to, with thecontent of the memory 204, and optionally with other componentsdescribed herein, to cause the apparatus 200 to select the identified atleast partially matching image as a next source image and compressremaining images (for example, images excluding the next source image)of the plurality of images based on the next source image. In anembodiment, the processor 202 is configured to, with the content of thememory 204, and optionally with other components described herein, tocause the apparatus 200 to identify an at least partially matching imageto the next source image from among the remaining images based on thecompression values. As explained above, the plurality of images arecompressed based on the source image and compression values aregenerated for the plurality of images. Based on the compression values,an at least partially matching image is identified for the source image.On identification of the at least partially matching image, theidentified image is selected as a next source image. The remainingimages of the plurality of images, for example, the plurality of imagesexcluding the identified image, may then be compressed based on the nextsource image. The compression may be performed as explained for thesource image. More specifically, the features may be extracted from thenext source image and quantized to generate a plurality of codescorresponding to the next source image. The next source image may thenbe compressed based on the plurality of codes (for example, using alossless compression scheme, such as the LZW compression scheme) togenerate a compression table. The remaining images may be compressedbased on the compression table corresponding to the next source image togenerate compression values corresponding to the remaining images. Animage associated with the highest compression value from among thecompression values may be identified as an at least partially matchingimage to the next source image.

In an example embodiment, at least partially matching images areidentified for the remaining images of the plurality of images. Forexample, the identified image may then be selected as a next sourceimage and an at least partially matching image identified from among theremaining images, for example, the plurality of images excluding thenext source image and the images for which at least partially matcheshave been previously identified. The steps of selecting, compressing andidentifying may be performed till at least partially matching images areidentified for the remaining images of the plurality of images. In anexample embodiment, a processing means may be configured to: select theat least partially matching image as a next source image, compressremaining images of the plurality of images based on the next sourceimage, and identify an at least partially matching image to the nextsource image from among the remaining images based on the compressionvalues. In an example embodiment, a processing means may be configuredto identify at least partially matching images for the remaining imagesof the plurality of images. An example of the processing means mayinclude the processor 202, which may be an example of the controller108.

In an example embodiment, the processor 202 is configured to, with thecontent of the memory 204, and optionally with other componentsdescribed herein, to cause the apparatus 200 to determine a spatialorder of the source image and the plurality of images based on theidentified at least partially matching images for the source image andthe plurality of images. It is noted that the plurality of imagesincludes the next source image and the remaining images as explainedabove. Further, as explained in the illustrative example, multipleimages corresponding to a scene may be captured by spanning an imagesensor, such as the image sensor 208, through various viewpoints andangles. The multiple images captured in such a manner may be consideredas spatially adjacent images and an spatial order of the captured imagesmay be determined by identifying at least partially matching images forthe source image and the plurality of images. The at least partiallymatching image identified for the source image may be considered tospatially adjacent image (for example, a neighboring image) to thesource image on account of having highest amount of overlapping imageportions and/or regions. The at least partially matching imagesidentified for the plurality of images similarly identify spatiallyadjacent images to each of those images, thereby enabling determining aspatial order of the captured images.

In an embodiment, the compression values corresponding to the imagescompressed based on the source image and/or the next source images maybe stored for enabling determination of the spatial order. For example,the compression values generated on compression of the plurality ofimages based on the source image may be represented by equation (1) as:

C(t=1)={Cs ₂ ,Cs ₃ , . . . , Cs _(k) , . . . ,Cs _(n)}  (1)

Where t corresponds to the iteration reference number. For example, forthe source image, a value of t may be equal to 1 as shown in equation(1). For the subsequently selected next source image, the value of t maybe equal to 2. The values Cs₂, Cs₃, . . . , Cs_(k), . . . , Cs_(n) maycorrespond to generated compression values associated with images 2, 3,K and N respectively, when compressed based on the source image. In anembodiment, the highest compression value may be identified by equation(2) as:

argmax(C)=Cs _(k)  (2)

Where argmax is a function for identifying the maximum (for example, thehighest) value among the compression values. If Cs_(k) is the highestcompression value from among the compression values, then associatedimage K may be identified as the at least partially matching image tothe source image. The highest compression value may be removed from theset of compression values depicted by equation (1). For example, C′ (1)may be configured by removing argmax (C)=Cs_(k) from C(1). Similarly,C(2 to N) may be obtained, on identification of the at least partiallymatching images, the compression values corresponding to the remainingimages may be stored, for example in memory 204. In an embodiment,determining the spatial order of the plurality of images comprisescomparing compression values of an image on compression based on thesource image and selected next source images. For example, if imagenumber 1 is the source image and images numbered 2, 3 and 4 are theselected next source images (for example, at least partially matchingimages to previous selected source images), then on determining imagenumber 5 to be at least partially matching to image number 4, acompression value of the image number 5 when compressed based on imagenumber 4 is compared with compressions values when the image number 5was compressed based on image numbers 1, 2 and 3 and 4. Based on thecomparison the spatial order of image number 5 is determined. Thedetermination of the spatial order of the source image and the pluralityof images is further explained in FIG. 4. In an example embodiment, aprocessing means may be configured to determine a spatial order of thesource image and the plurality of images based on the identified atleast partially matching images for the source image and the pluralityof images. An example of the processing means may include the processor202, which may be an example of the controller 108.

In an example embodiment, the processor 202 is configured to, with thecontent of the memory 204, and optionally with other componentsdescribed herein, to cause the apparatus 200 to generate a panoramaimage based at least on stitching the source image and the plurality ofimages arranged in the determined spatial order. As described herein,the term ‘panorama image’ refers to an image associated with a wider orelongated field of view. A panorama image may include a two-dimensional(2-D) construction of a three-dimensional (3-D) scene. In someembodiments, the panorama image may provide about 360 degrees view ofthe scene. In an embodiment, on determining the spatial order, thesource image and the plurality of images may be arranged in the spatialorder as spatially adjacent images and stitched (for example, combined)to generate the panorama image.

In an example embodiment, the processor 202 is configured to, with thecontent of the memory 204, and optionally with other componentsdescribed herein, to cause the apparatus 200 to perform at least one ofa feature extraction and a registration across the source image and theplurality of images prior to stitching the source image and theplurality of images arranged in the determined spatial order. In anexample embodiment, the feature extraction and registration may beperformed across an image (for example, source image or next sourceimage) and the corresponding at least partially matching image. Thefeature extraction of an image and at least partially matching image maybe performed using algorithms such as scale-invariant feature transform(SIFT), Harris corner detector, smallest univalue segment assimilatingnucleus (SUSAN) corner detector, features from accelerated segment test(FAST). Alternatively, extracting the features may be performed byapplying one of DCT, DST, KLT transform and a Hadamard transform onmacroblocks corresponding to the image and the corresponding at leastpartially matching image. In an example embodiment, the DCT is appliedon macroblocks corresponding to the image and the corresponding at leastpartially matching image and the DC components thus obtained may betreated as features. In an embodiment, the DC components may be obtainedby partially decoding the source image and the plurality of images.

In an embodiment, registration may refer to process wherein objectmotion may be separated from motion of media capture medium and allbackground pixels may be made static in a common image frame. In anembodiment, the registration may be performed to align similar contentacross an image (for example, source image or next source image) and thecorresponding at least partially matching image and remove jitterintroduced either on account of movement of media capture medium (e.g.,from being handheld) or account of transient environmental conditions,such as high wind conditions, during the capture of the multimediacontent. Two-dimensional (2D) and three-dimensional (3D) imagestabilization algorithms may be employed for performing theregistration. In an embodiment, the 2D algorithms may estimate cameramotion in the 2D image plane motion and zoom or crop to compensate. Themotion may be evaluated in a variety of ways, including optical flow,stable feature points, and block-based cross-correlation. In anembodiment, 3D video stabilization algorithms may identify stable 3Dfeature points by structure-from-motion and apply image based or warpingtechniques to cope with parallax effect.

In an example embodiment, the stitching the source image and theplurality of images arranged in the determined spatial order may involveperforming at least one of warping and blending of images to generatethe panorama image. Warping, for example alignment, may be performed inorder to obtain an image in view of other image. This may be done as thetwo images, even though capturing the same scene may capture theinformation with slight difference on account of difference in an angleof capture. Accordingly, alignment of pixels may be performed to obtainthe view of one image in terms of other image. Accordingly, a warp maybe computed to obtain one image in the view of the other image. In someembodiments, warping may involve performing interpolation for example,bilinear interpolation. In an example embodiment, the warp may becomputed as an 8 parameter transform (for example, using standardtechniques, such as similarity, homography, affine and the like) or maybe computed using a dense correspondence computed for a stereo camera.In an example embodiment, the warped images may be stitched by computinga seam between the images and blending the images across the seam. Theimage blending techniques may involve cross-fading or morphing acrosstransitions. The identification of the at least partially matching imagefor facilitating applications, such as panorama image generation isfurther explained in FIG. 3.

FIG. 3 illustrates a simplified logical overview 300 for identifying anat least partially matching image in accordance with an exampleembodiment. At 302, multiple images such as images 304 a, 304 b, 304 c,304 d, 304 e, 304 f, 304 g to 304 n (depicted as images I₁-I_(n)) arereceived. In an embodiment, the multiple images may correspond to ascene captured by an image sensor, such as the image sensor 208, byspanning through various viewpoints and angles. Accordingly, themultiple images may include overlapping image portions corresponding tothe scene. The image with the initial timestamp may be chosendynamically to be the first image. At 306, the first image from amongthe multiple images may be chosen as the source image. In FIG. 4, theimage 304 a is depicted to be chosen as the source image at 306. Theremaining images 304 b-304 n may configure the plurality of images forpurposes of this illustration. In an embodiment, a user may provide theselection of source image 304 a from among collection of images. Theremaining images in the collection of images, for example images 304b-304 n, may configure the plurality of images. In an embodiment, theplurality of images may be sourced from the network, such as the networkexplained in FIG. 2, on provision of the source image 304 a by a user.

At 308, the plurality of images is compressed based on the source image304 a. As explained in FIG. 2, features may be extracted from the sourceimage 304 a and the extracted features may be quantized to generate aplurality of codes corresponding to the source image 304 a. The sourceimage 304 a may be compressed based on the plurality of codes using alossless compression scheme, such as LZW, to generate a compressiontable. The plurality of images may be compressed corresponding to thesource image 304 a using the generated compression table. Oncompression, each image of the plurality of images may be associatedwith a compression value. The compression value may be indicative of theextent of compression achieved on compression based on the source image304 a. In FIG. 3, the curved arrows 310-322 depict relation betweenimages, for example a compression of an image of the plurality of imagesbased on the source image 304 a, whereas a value depicted to beassociated with each curved arrow corresponds to a generated compressionvalue. For example, curved arrow 310 depicts compression of image 304 b(for example, image I₂) based on image 304 a and ‘C₁₂’ is a generatedcompression value associated with image 304 b on compression based onimage 304 a. Similarly, curved arrows 312, 314, 316, 318, 320 and 322depict compression of images 304 c, 304 d, 304 e, 304 f, 304 g and 304 nbased on source image 304 a, respectively. The compression values ‘C₁₃’,‘C₁₄’, ‘C₁₅’, ‘C₁₆’, ‘C₁₇’ and ‘C_(1n)’ correspond to compression valuesgenerated for images 304 c, 304 d, 304 e, 304 f, 304 g to 304 n oncompression based on the source image 304 a.

In an embodiment, the compression values associated with the images 304b-304 n may be compared and an image among the plurality of images withthe highest compression value among the compression values C_(12 . . .)-C_(1n) may be identified as at least partially matching image to sourceimage 304 a. In an embodiment depicted by FIG. 3, compression value C₁₅is considered to be the highest compression value among the compressionvalues C₁₂-C_(1n). Accordingly, image 304 e (for example, image I₅) isidentified as at least partially matching image to the source image 304a at 330. As explained in FIG. 2, a higher compression value correspondsto a higher similarity (for example, higher amount of overlapping imageportions and/or regions) between the source image and the imagesubjected to compression, while a lower compression value corresponds toa fewer similarities (for example, lesser amount of overlapping imageportions and/or regions) between the source image and the imagesubjected to compression. In FIG. 3, image 304 e has the highestcompression value from among the compression values associated with theplurality of images and accordingly is considered to be at leastpartially matching, relatively similar to the source image 304 a. Theidentification of the at least partially matching image in such a mannermay facilitate enablement of a variety of applications. For example, auser may provide the choice of a source image and identify a closestmatching match from the web. In an embodiment, identifying at leastpartially matching images may help in retrieving similar images from alarge collection of images or help remove duplicates therebyfacilitating faster browsing and better utilization of storage space.

In an embodiment, on identification of image 304 e as at least partiallymatching image to the source image 304 a, the image 304 e is selected asthe next source image and remaining images, for example images 304 b to304 n excluding image 304 e, are compressed based on image 304 e toidentify at least partially matching image to the next source image 304e. The steps of selecting the next source image, compressing theremaining images based on the next source image and identifying the atleast partially matching image may be repeated till at least partiallymatching images are identified for the plurality of image 304 b-304 n. Aspatial order of the images may be determined for generating thepanorama image based on identified at least partially matching imagesfor the source image 304 a and the plurality of images 304 b-304 n. Thedetermination of the spatial order is explained in FIG. 4.

FIG. 4 illustrates determination of a spatial order of images forpanorama image generation in accordance with an example embodiment. InFIG. 4, determination of spatial order of an image from among themultiple images 304 a-304 n of FIG. 3 is depicted for illustrationpurposes. As explained in FIG. 3, at least partially matching images areidentified for the source image 304 a and the plurality of images. In anembodiment, the spatial order may be determined subsequent todetermination of the at least partially matching images for the sourceimage 304 a and the plurality of images 304 n. Alternatively, a spatialposition of image may be determined in an on-going manner and subsequentto identification of the image as the at least partially matching image.In FIG. 4, the spatial position of the image is depicted to bedetermined subsequent to the identification of the image as the at leastpartially matching image. It is noted that determining the spatialposition of images of the plurality of images determines the spatialorder and thereby facilitates in generation of the panorama image. In anembodiment, the determination of the spatial order and the subsequentpanorama image generation may be performed if it is determined that thesource image and the plurality of images include at least 20%overlapping image portions and/or regions. In some embodiments, thepanorama generation may be performed even if it is determined that thesource image and the plurality of images include less than 20%overlapping image portions and/or regions

In FIG. 4, at 410, images 304 a, 304 e, 304 d and 304 g are depicted toarranged in their respective spatial positions. On selection of theimage 304 g as the next source image, remaining images from theplurality of images may be compressed to identify the at least partiallymatching image to image 304 g. In FIG. 4, image 304 b is depicted to beidentified as the at least partially matching image to image 304 g. Onidentification, a compression value associated with the image 304 b forcompression based on image 304 g, for example compression value C₇₂ (forexample, compression value depicted to be associated with arrow 332illustrating compression of image 304 b based on images 304 g)), iscompared with compression values C₁₂, C₅₂ and C₄₂ (for example,compression values depicted to be associated with arrows 324, 326 and328 illustrating compression of image 304 b based on images 304 a, 304 eand 304 d) and accordingly the spatial position of image 304 b isdetermined. It is noted that the compression values C₁₂, C₅₂ and C₄₂were computed during compression of images for previously selectedsource image/next source images. For example, C₁₂ was computed duringcompression of plurality of images based on source image 304 a, whileC₅₂ and C₄₂ were computed during compression of remaining images basedon images 304 e and 304 d respectively. In an embodiment, on compressingimages and identifying an image with the highest compression value, theremaining compression values may be stored for facilitatingdetermination of the spatial order. The storing of the remainingcompression values may be performed as explained in FIG. 2.

In the embodiment depicted in FIG. 4, C₁₂ is considered to be greaterthan C₇₂ implying a higher amount of overlap between images 304 a andimage 304 b than between images 304 g and 304 b. For example, an overlapbetween image images 304 a and 304 b may be 40% implying 40% overlappingimage portions and/or regions while an overlap between images 304 g and304 b may be 30%. Accordingly, a spatial position of the image 304 b maybe determined to be adjacent to image 304 a as depicted in the spatialorder at 420. It is noted that the spatial position may be closer to animage with which the image has a maximum overlap. Further it is notedthat a compression value between images 304 a and 304 b though greaterthan the compression value between images 304 g and 304 b may not behighest and accordingly image 304 e was initially chosen to be at leastpartially matching to image 304 a.

It is noted that FIG. 4, depicts a single row (1-D) construction of theorder for panorama image generation. In an embodiment, the panoramaimage generation may include both horizontal and vertical translationsand accordingly, the spatial order determination may include determininga spatial position of images on left/right as well as top and bottomsides of images. On determining the spatial order, the source image andthe plurality of images may be arranged in spatial order. As explainedin FIG. 2, feature extraction and registration may be performed betweenan image and corresponding at least partially matching image prior towarping and blending the images for panorama generation. A method forprocessing of images is explained in FIG. 5.

FIG. 5 is a flowchart depicting an example method 500 for processing ofimages in accordance with an example embodiment. The method 500 depictedin flow chart may be executed by, for example, the apparatus 200 of FIG.2. Operations of the flowchart, and combinations of operation in theflowchart, may be implemented by various means, such as hardware,firmware, processor, circuitry and/or other device associated withexecution of software including one or more computer programinstructions. For example, one or more of the procedures described invarious embodiments may be embodied by computer program instructions. Inan example embodiment, the computer program instructions, which embodythe procedures, described in various embodiments may be stored by atleast one memory device of an apparatus and executed by at least oneprocessor in the apparatus. Any such computer program instructions maybe loaded onto a computer or other programmable apparatus (for example,hardware) to produce a machine, such that the resulting computer orother programmable apparatus embody means for implementing theoperations specified in the flowchart. These computer programinstructions may also be stored in a computer-readable storage memory(as opposed to a transmission medium such as a carrier wave orelectromagnetic signal) that may direct a computer or other programmableapparatus to function in a particular manner, such that the instructionsstored in the computer-readable memory produce an article of manufacturethe execution of which implements the operations specified in theflowchart. The computer program instructions may also be loaded onto acomputer or other programmable apparatus to cause a series of operationsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructions, whichexecute on the computer or other programmable apparatus provideoperations for implementing the operations in the flowchart. Theoperations of the method 500 are described with help of apparatus 200 ofFIG. 2. However, the operations of the method can be described and/orpracticed by using any other apparatus.

At block 502 of method 500, receipt of a source image is facilitated. Inan embodiment, an image for use in processing a set of images (forexample, images with or without overlapping image portions and/orregions) may be referred to as a source image. In an embodiment, thesource image may be an image captured corresponding to a scene by amedia capture element, such as a camera module 122 of device 100 or theimage sensor 208. The scene may include one or more objects in asurrounding environment of the media capture element, for example, aperson or a gathering of individuals, birds, books, a playground,natural scenery, such as a mountain, and the like. In an embodiment,multiple images corresponding to a scene may be captured by an imagesensor. An initial image (for example, the image with the earliesttimestamp) from among the captured images may be dynamically chosen asthe source image. In an embodiment, a source image may be selected fromamong a collection of images either automatically or manually by userselection. In an embodiment, a user may provide a selection of thesource image from among a collection of images, for example by using auser interface, such as the user interface 206. In an embodiment, thesource image may be received from an internal memory such as hard drive,random access memory (RAM) or from an external storage medium such asdigital versatile disk, compact disk, flash drive, memory card, or fromexternal storage locations through Internet, Bluetooth®, and the like.

At block 504, a plurality of images is compressed based on the sourceimage. In an embodiment, the image sensor may capture imagescorresponding to a scene from various angles and viewpoints. The variousangles and viewpoints for the capturing the images corresponding to thescene may be achieved by spanning the image sensor in one or moredirections. In an example embodiment, the image sensor may be spannedthrough 360 degrees during image capture to obtain multiple imagescorresponding to the scene. The captured images may include overlappingimage portions and/or regions on account of the images corresponding tothe same scene being captured from different angles and viewpoints. Theinitial captured image from among the multiple images may be chosen asthe source image whereas the remaining images of the multiple capturedimages may configure the plurality of images to be compressed based onthe source image. It is noted that the source image and the plurality ofimages together configure the images captured by the image sensorcorresponding to the scene and comprise overlapping image portionsand/or regions. In an embodiment, the source image may be chosen from acollection of images stored in memory location, and one or more imagesfrom the remaining images from the collection may configure theplurality of images to be compressed based on the source image. In anembodiment, the source image may be received from a user and theplurality of images may be received via network. Examples of the networkmay include a wired network, a wireless network and/or a combinationthereof. Examples of the wired network include but are not limited tolocal area network, wide area network, Ethernet and the like. Examplesof the wireless network include but are not limited to cellular network,Wi-Fi network, wireless LAN, Zigbee network and the like. An example ofcombination of the wired network and the wireless network may includebut is not limited to the Internet.

In an example embodiment, compressing the plurality of images based onthe source image comprises extracting features from the source image andquantizing the extracted features to create a plurality of codescorresponding to the source image. In an embodiment, the extractedfeatures are capable of characterizing the source image. Examples of thefeature points may include, but are not limited to, corners, edges of animage, or other region of interest such as background of the scene. Inan example embodiment, the apparatus 200 may be caused to use algorithmssuch as scale-invariant feature transform (SIFT), Harris cornerdetector, smallest univalue segment assimilating nucleus (SUSAN) cornerdetector, features from accelerated segment test (FAST) for determiningfeature points associated with the source image. In an embodiment,extracting the features from the source image comprises applying adiscrete cosine transform (DCT), a discrete sine transform (DST), aKarhunen-Loève theorem (KLT) transform or a Hadamard transform onmacroblocks corresponding to the source image. In an example embodiment,the DCT is applied on macroblocks corresponding to the source image andthe DC components thus obtained may be treated as features correspondingto the source image. In an embodiment, the DC components may be obtainedby partially decoding the source image. On extracting the features ofthe source image, the features may be subjected to quantization, whereeach feature is scaled corresponding to a scale-down factor (alsoreferred to as the quantization parameter) to generate an optimal set ofplurality of codes.

In an embodiment, compressing the plurality of images based on thesource image further comprises generating a compression table bycompressing the source image based on the plurality of codes. In anembodiment, the source image is compressed using a lossless compressionscheme for generating the compression table from the plurality of codes.In an embodiment, the lossless compression scheme is an arithmeticcoding scheme, Huffman coding scheme, context coding scheme, Lempel-Zivcompression scheme or Lempel-Ziv-Welch (LZW) compression scheme. Forexample, the source image and the plurality of codes generated byquantizing the features corresponding to the source image may beprovided as an input to LZW compression scheme, which may provide anoutput as the compression table. In an embodiment, the compression tablemay comprise a pattern of matrix of coefficients characterizing thesource image. In an embodiment, the compression table is constructed byscanning in a plurality of directions. For example, in an embodiment thecompression table may be constructed by scanning in non-orthogonaldirections in addition to a horizontal direction and a verticaldirection.

In an embodiment, the plurality of images is compressed based on thegenerated compression table corresponding to the source image. Asexplained above, the compression table is generated based on compressingthe source image based on the plurality of codes. The compression tableand an image of the plurality of images may be provided as an input to alossless compression scheme, such as the LZW compression scheme, tocompress the image. The plurality of images may be compressed in asimilar manner based on the generated compression table. In anembodiment, an image may be associated with a compression valuegenerated on compression of the image based on the compression table. Inan embodiment, the compression value may be configured to provide anindication of the extent of compression achieved based on thecompression table corresponding to the source image. In an embodiment, ahigher compression value corresponds to a higher similarity (forexample, higher amount of overlapping image portions and/or regions)between the source image and the image subjected to compression.Similarly, a lower compression value corresponds to a fewer similarities(for example, lesser amount of overlapping image portions and/orregions) between the source image and the image subjected tocompression. On performing compression of the plurality of images basedon the source image, a plurality of compression values may be generated.In an embodiment, each image of the plurality of images may beassociated with a compression value generated on compression of the eachimage. In an embodiment, one or more images of the plurality of imagesmay be associated with compression values generated on compression ofthe one or more images.

At block 506, an at least partially matching image is identified to thesource image from among the plurality of images based on compressionvalues. As explained, the plurality of images may be compressed based onthe source image and the compressed images may be associated withcompression values. As the compression values provide an indication ofthe extent of compression achieved, the image with the highestcompression value may be treated as the image to be at least partiallymatching image, for example relatively similar, to the source image. Inan embodiment, the at least partially matching image is identified basedon a highest compression value from among the compression values. It isnoted that the at least partially matching image may include overlappingimage portions and/or regions with the source image to a degree higherthan the remaining images from among the plurality of images. Theidentification of the at least partially matching image may be performedas explained in FIG. 3.

In an example embodiment, method 500 may further include selecting theat least partially matching image as a next source image, compressingremaining images of the plurality of images based on the next sourceimage, and identifying an at least partially matching image to the nextsource image from among the remaining images based on the compressionvalues. As explained above, the plurality of images are compressed basedon the source image and compression values are generated for theplurality of images. Based on the compression values, an at leastpartially matching image is identified for the source image. Onidentification of the at least partially matching image, the identifiedimage is selected as a next source image. The remaining images of theplurality of images, for example, the plurality of images excluding theidentified image, may then be compressed based on the next source image.The compression may be performed as explained for the source image. Morespecifically, the features may be extracted from the next source imageand quantized to generate a plurality of codes corresponding to the nextsource image. The next source image may then be compressed based on theplurality of codes (for example, using a lossless compression scheme,such as the LZW compression scheme) to generate a compression table. Theremaining images may be compressed based on the compression tablecorresponding to the next source image to obtain compression valuescorresponding to the remaining images. An image with the highestcompression value from among the compression values may be identified asan at least partially matching image to the next source image.

In an example embodiment, at least partially matching images areidentified for the remaining images of the plurality of images. Forexample, the identified image may then be selected as a next sourceimage and an at least partially matching image identified from among theremaining images, for example, the plurality of images excluding thenext source image and the images for which at least partially matcheshave been previously identified. The steps of selecting, compressing andidentifying may be performed till at least partially matching images areidentified for the remaining images of the plurality of images.

In an example embodiment, the method 500 may further include determininga spatial order of the source image and the plurality of images based onthe identified at least partially matching images for the source imageand the plurality of images. It is noted that the plurality of imagesincludes the next source image and the remaining images as explainedabove. The spatial order may be determined as explained in FIG. 4. In anexample embodiment, a panorama image may be generated based at least onstitching the source image and the plurality of images arranged in thedetermined spatial order. In an example embodiment, at least one of afeature extraction and a registration across the source image and theplurality of images may be performed prior to stitching the source imageand the plurality of images arranged in the determined spatial order.Another method for processing of images is explained in detail withreference to FIGS. 6A and 6B.

FIGS. 6A and 6B illustrate a flowchart depicting an example method 600for processing of images in accordance with another example embodiment.The apparatus 200 of FIG. 2 may employ the method 600 for processing ofimages. At block 602 of method 600, images captured corresponding to ascene are received. The images corresponding to the scene are capturedfrom a plurality of angles and viewpoints. In an example embodiment, theimages may be received from an image sensor, such as the image sensor208. In an embodiment, the image sensor may be spanned through 360degrees during capturing the images corresponding to the scene. At block604, a first image from among the received images is selected as thesource image. The remaining images from the received images mayconfigure the plurality of images.

At block 606, a compression table is generated based on the sourceimage. As explained in FIG. 2, features may be extracted from the sourceimage and quantized to generate a plurality of codes. The source imagemay be compressed based on the plurality of codes to generate thecompression table. In an embodiment, lossless compression schemes, suchas LZW compression scheme, may be utilized for compressing the sourceimage based on the plurality of codes to generate the compression table.

At block 608, a plurality of images is compressed based on thecompression table corresponding to the source image. Each image of theplurality of images is associated with a compression value generated oncompression based on the source image. At block 610, an at leastpartially matching image to the source image is identified from amongthe plurality of images based on the compression values. In an exampleembodiment, an image with a highest compression value from among thecompression values may be identified as an at least partially matchingimage to the next source image. The identification of the at leastpartially matching image to the source image may be performed asexplained in FIG. 3.

At block 612, it is checked whether at least partially matching imagesare identified for all the images. If it is determined that at leastpartially matching images are not identified for all the images then, atblock 614, the identified at least partially matching image is selectedas the next source image. The operations performed at the blocks 606,608, 610 and 612 are repeated till at least partially matching imagesfor all the plurality of images. If it is determined that at leastpartially matching images are identified for all the images then, atblock 616, then a spatial order of the source image and the plurality ofimages is determined based on the identified at least partially matchingimages for the source image and the plurality of images. Thedetermination of the spatial order may be performed as explained in FIG.4.

At block 618, at least one of a feature extraction and a registrationacross the source image and the plurality of images are performed. In anexample embodiment, the feature extraction and registration may beperformed across an image (for example, source image or next sourceimage) and the corresponding at least partially matching image. Thefeature extraction of an image and at least partially matching image maybe performed using algorithms such as scale-invariant feature transform(SIFT), Harris corner detector, smallest univalue segment assimilatingnucleus (SUSAN) corner detector, features from accelerated segment test(FAST). Alternatively, extracting the features may be performed byapplying one of DCT, DST, KLT transform and a Hadamard transform onmacroblocks corresponding to the image and the corresponding at leastpartially matching image. In an example embodiment, the DCT is appliedon macroblocks corresponding to the image and the corresponding at leastpartially matching image and the DC components thus obtained may betreated as features. In an embodiment, the DC components may be obtainedby partially decoding the source image and the plurality of images.

In an example embodiment, two-dimensional (2D) and three-dimensional(3D) image stabilization algorithms may be employed for performing theregistration. In an embodiment, the 2D algorithms may estimate cameramotion in the 2D image plane motion and zoom or crop to compensate. Themotion may be evaluated in a variety of ways, including optical flow,stable feature points, and block-based cross-correlation. In anembodiment, 3D video stabilization algorithms may identify stable 3Dfeature points by structure-from-motion and apply image based or warpingtechniques to cope with parallax effect.

At block 620, a panorama image is generated based at least on stitchingthe source image and the plurality of images arranged in the determinedspatial order. In an example embodiment, the stitching the source imageand the plurality of images arranged in the determined spatial order mayinvolve performing at least one of warping and blending of images togenerate the panorama image. Warping, for example alignment may beperformed in order to obtain an image in view of other image. This maybe done as the two images, even though capturing the same scene maycapture the information with slight difference on account of differencein an angle of capture. Accordingly, alignment of pixels may beperformed to obtain the view of one image in terms of other image.Accordingly, a warp may be computed to obtain one image in the view ofthe other image. In some embodiments, warping may involve performinginterpolation for example, bilinear interpolation. In an exampleembodiment, the warp may be computed as an 8 parameter transform (forexample, using standard techniques, such as similarity, homography,affine and the like) or may be computed using a dense correspondencecomputed for a stereo camera. In an example embodiment, the warpedimages may be stitched by computing a seam between the images andblending the images across the seam. The image blending techniques mayinvolve cross-fading or morphing across transitions.

To facilitate discussion of the methods 500 and/or 600 of FIGS. 5, 6Aand 6B, certain operations are described herein as constituting distinctsteps performed in a certain order. Such implementations are exemplaryand non-limiting. Certain operation may be grouped together andperformed in a single operation, and certain operations can be performedin an order that differs from the order employed in the examples setforth herein. Moreover, certain operations of the methods 500 and/or 600are performed in an automated fashion. These operations involvesubstantially no interaction with the user. Other operations of themethods 500 and/or 600 may be performed by in a manual fashion orsemi-automatic fashion. These operations involve interaction with theuser via one or more user interface presentations.

Without in any way limiting the scope, interpretation, or application ofthe claims appearing below, a technical effect of one or more of theexample embodiments disclosed herein is to perform processing of images.As explained in FIGS. 2-6B, the processing of images may involve enableidentifying at least partially matching images, for example closeneighbors/adjacent images from a set of images of scene taken at variousangles and viewpoints, which may be used sequentially to stitch andcreate a 1D or multi-row (2D) panoramic view (complete picture of thescene covering the maximum of angle of 360 degree view) of a scene. Theidentification of close neighbors in such a manner precludes the need toperform explicit registration of images and in general provides a fastand robust technique for automatic and sequential panorama generation.Further, a user may provide the choice of a source image and identify aclosest matching match from among the plurality of images on the web. Inan embodiment, identifying at least partially matching images may helpin retrieving similar images from a large collection of images, helpremove duplicates thereby facilitating faster browsing and betterutilization of storage space.

Various embodiments described above may be implemented in software,hardware, application logic or a combination of software, hardware andapplication logic. The software, application logic and/or hardware mayreside on at least one memory, at least one processor, an apparatus or,a computer program product. In an example embodiment, the applicationlogic, software or an instruction set is maintained on any one ofvarious conventional computer-readable media. In the context of thisdocument, a “computer-readable medium” may be any media or means thatcan contain, store, communicate, propagate or transport the instructionsfor use by or in connection with an instruction execution system,apparatus, or device, such as a computer, with one example of anapparatus described and depicted in FIGS. 1 and/or 2. Acomputer-readable medium may comprise a computer-readable storage mediumthat may be any media or means that can contain or store theinstructions for use by or in connection with an instruction executionsystem, apparatus, or device, such as a computer.

If desired, the different functions discussed herein may be performed ina different order and/or concurrently with each other. Furthermore, ifdesired, one or more of the above-described functions may be optional ormay be combined.

Although various aspects of the embodiments are set out in theindependent claims, other aspects comprise other combinations offeatures from the described embodiments and/or the dependent claims withthe features of the independent claims, and not solely the combinationsexplicitly set out in the claims.

It is also noted herein that while the above describes exampleembodiments of the invention, these descriptions should not be viewed ina limiting sense. Rather, there are several variations andmodifications, which may be made without departing from the scope of thepresent disclosure as defined in the appended claims.

We claim:
 1. A method comprising: facilitating receipt of a sourceimage; compressing a plurality of images based on the source image,wherein the plurality of images are associated with compression valuesgenerated on compression of the plurality of images; and identifying anat least partially matching image to the source image from among theplurality of images based on the compression values.
 2. The method asclaimed in claim 1, further comprising: selecting the identified atleast partially matching image as a next source image; compressingremaining images of the plurality of images based on the next sourceimage, wherein the remaining images are associated with compressionvalues generated on compression of the remaining images; and identifyingan at least partially matching image to the next source image from amongthe remaining images based on the compression values.
 3. The method asclaimed in claim 2, further comprising: identifying at least partiallymatching images for the remaining images of the plurality of images. 4.The method as claimed in claim 1, wherein the at least partiallymatching image is identified based on a highest compression value fromamong the compression values.
 5. The method as claimed in claim 1,wherein the source image and the plurality of images correspond toimages captured corresponding to a scene and comprise overlapping imageportions or regions.
 6. The method as claimed in claim 3, furthercomprising: determining a spatial order of the source image and theplurality of images based on the identified at least partially matchingimages for the source image and the plurality of images; and generatinga panorama image based at least on stitching the source image and theplurality of images arranged in the determined spatial order.
 7. Themethod as claimed in claim 6, wherein determining the spatial order ofthe plurality of images comprises comparing compression valuesassociated with an image on compression based on the source image andselected one or more next source images.
 8. The method as claimed inclaim 6, further comprising: performing at least one of a featureextraction and a registration across the source image and the pluralityof images prior to stitching the source image and the plurality ofimages arranged in the determined spatial order.
 9. The method asclaimed in claim 1, wherein compressing the plurality of images based onthe source image further comprises: extracting features from the sourceimage, wherein the features are capable of characterizing the sourceimage; quantizing the extracted features to create a plurality of codescorresponding to the source image; and generating a compression table bycompressing the source image based on the plurality of codes, whereinthe plurality of images is compressed based on the generated compressiontable corresponding to the source image.
 10. An apparatus comprising: atleast one processor; and at least one memory comprising computer programcode, the at least one memory and the computer program code configuredto, with the at least one processor, cause the apparatus to at leastperform: facilitate receipt of a source image; compress a plurality ofimages based on the source image, wherein the plurality of images areassociated with compression values generated on compression of theplurality of images; and identify an at least partially matching imageto the source image from among the plurality of images based on thecompression values.
 11. The apparatus as claimed in claim 10, whereinthe apparatus is further caused, at least in part, to: select theidentified at least partially matching image as a next source image;compress remaining images of the plurality of images based on the nextsource image, wherein the remaining images are associated withcompression values generated on compression of the remaining images; andidentify an at least partially matching image to the next source imagefrom among the remaining images based on the compression values.
 12. Theapparatus as claimed in claim 11, wherein the apparatus is furthercaused, at least in part, to: identify at least partially matchingimages for the remaining images of the plurality of images.
 13. Theapparatus as claimed in claim 10, wherein the at least partiallymatching image is identified based on a highest compression value fromamong the compression values.
 14. The apparatus as claimed in claim 10,wherein the source image and the plurality of images correspond toimages captured corresponding to a scene and comprise overlapping imageportions or regions.
 15. The apparatus as claimed in claim 12, whereinthe apparatus is further caused, at least in part, to: determine aspatial order of the source image and the plurality of images based onthe identified at least partially matching images for the source imageand the plurality of images; and generate a panorama image based atleast on stitching the source image and the plurality of images arrangedin the determined spatial order.
 16. The apparatus as claimed in claim15, wherein determining the spatial order of the plurality of imagescomprises comparing compression values associated with an image oncompression based on the source image and selected one or more nextsource images.
 17. The apparatus as claimed in claim 15, wherein theapparatus is further caused, at least in part, to: perform at least oneof a feature extraction and a registration across the source image andthe plurality of images prior to stitching the source image and theplurality of images arranged in the determined spatial order.
 18. Theapparatus as claimed in claim 10, wherein to compress the plurality ofimages based on the source image, the apparatus is further caused, atleast in part to: extract features from the source image, wherein thefeatures are capable of characterizing the source image; quantize theextracted features to create a plurality of codes corresponding to thesource image; and generate a compression table by compressing the sourceimage based on the plurality of codes, wherein the plurality of imagesis compressed based on the generated compression table corresponding tothe source image.
 19. A computer program product comprising at least onecomputer-readable storage medium, the computer-readable storage mediumcomprising a set of instructions, which, when executed by one or moreprocessors, cause an apparatus to at least perform: facilitate receiptof a source image; compress a plurality of images based on the sourceimage, wherein the plurality of images are associated with compressionvalues generated on compression of the plurality of images; and identifyan at least partially matching image to the source image from among theplurality of images based on the compression values.
 20. The computerprogram product as claimed in claim 19, wherein the apparatus is furthercaused, at least in part, to: select the identified at least partiallymatching image as a next source image; compress remaining images of theplurality of images based on the next source image, wherein theremaining images are associated with compression values generated oncompression of the remaining images; and identify an at least partiallymatching image to the next source image from among the remaining imagesbased on the compression values.